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  • richardmitnick 8:35 am on April 6, 2020 Permalink | Reply
    Tags: "Accelerating data-driven discoveries", Michael Stonebraker, MIT, Paradigm4, Paradigm’s REVEAL and SciDB products take care of all the data wrangling., research institutes, The company founded by Marilyn Matz SM ’80 and Michael Stonebraker helps pharmaceutical companies; research institutes; and biotech companies turn data into insights., Today Paradigm4’s customers include some of the biggest pharmaceutical and biotech companies in the world as well as research labs at the National Institutes of Health.   

    From MIT News: “Accelerating data-driven discoveries” 

    MIT News

    From MIT News

    April 4, 2020
    Zach Winn

    1
    Paradigm4 allows users to integrate data from sources like genomic sequencing, biometric measurements, environmental factors, and more into their inquiries to enable new discoveries across a range of life science fields.

    Life science companies use Paradigm4’s unique database management system to uncover new insights into human health.

    As technologies like single-cell genomic sequencing, enhanced biomedical imaging, and medical “internet of things” devices proliferate, key discoveries about human health are increasingly found within vast troves of complex life science and health data.

    But drawing meaningful conclusions from that data is a difficult problem that can involve piecing together different data types and manipulating huge data sets in response to varying scientific inquiries. The problem is as much about computer science as it is about other areas of science. That’s where Paradigm4 comes in.

    The company, founded by Marilyn Matz SM ’80 and Turing Award winner and MIT Professor Michael Stonebraker, helps pharmaceutical companies, research institutes, and biotech companies turn data into insights.

    It accomplishes this with a computational database management system that’s built from the ground up to host the diverse, multifaceted data at the frontiers of life science research. That includes data from sources like national biobanks, clinical trials, the medical internet of things, human cell atlases, medical images, environmental factors, and multi-omics, a field that includes the study of genomes, microbiomes, metabolomes, and more.

    On top of the system’s unique architecture, the company has also built data preparation, metadata management, and analytics tools to help users find the important patterns and correlations lurking within all those numbers.

    In many instances, customers are exploring data sets the founders say are too large and complex to be represented effectively by traditional database management systems.

    “We’re keen to enable scientists and data scientists to do things they couldn’t do before by making it easier for them to deal with large-scale computation and machine-learning on diverse data,” Matz says. “We’re helping scientists and bioinformaticists with collaborative, reproducible research to ask and answer hard questions faster.”

    A new paradigm

    Stonebraker has been a pioneer in the field of database management systems for decades. He has started nine companies, and his innovations have set standards for the way modern systems allow people to organize and access large data sets.

    Much of Stonebraker’s career has focused on relational databases, which organize data into columns and rows. But in the mid 2000s, Stonebraker realized that a lot of data being generated would be better stored not in rows or columns but in multidimensional arrays.

    For example, satellites break the Earth’s surface into large squares, and GPS systems track a person’s movement through those squares over time. That operation involves vertical, horizontal, and time measurements that aren’t easily grouped or otherwise manipulated for analysis in relational database systems.

    Stonebraker recalls his scientific colleagues complaining that available database management systems were too slow to work with complex scientific datasets in fields like genomics, where researchers study the relationships between population-scale multi-omics data, phenotypic data, and medical records.

    “[Relational database systems] scan either horizontally or vertically, but not both,” Stonebraker explains. “So you need a system that does both, and that requires a storage manager down at the bottom of the system which is capable of moving both horizontally and vertically through a very big array. That’s what Paradigm4 does.”

    In 2008, Stonebraker began developing a database management system at MIT that stored data in multidimensional arrays. He confirmed the approach offered major efficiency advantages, allowing analytical tools based on linear algebra, including many forms of machine learning and statistical data processing, to be applied to huge datasets in new ways.

    Stonebraker decided to spin the project into a company in 2010, when he partnered with Matz, a successful entrepreneur who co-founded Cognex Corporation, a large industrial machine-vision company that went public in 1989. The founders and their team went to work building out key features of the system, including its distributed architecture that allows the system to run on low-cost servers, and its ability to automatically clean and organize data in useful ways for users.

    The founders describe their database management system as a computational engine for scientific data, and they’ve named it SciDB. On top of SciDB, they developed an analytics platform, called the REVEAL discovery engine, based on users’ daily research activities and aspirations.

    “If you’re a scientist or data scientist, Paradigm’s REVEAL and SciDB products take care of all the data wrangling and computational ‘plumbing and wiring,’ so you don’t have to worry about accessing data, moving data, or setting up parallel distributed computing,” Matz says. “Your data is science-ready. Just ask your scientific question and the platform orchestrates all of the data management and computation for you.”

    SciDB is designed to be used by both scientists and developers, so users can interact with the system through graphical user interfaces or by leveraging statistical and programming languages like R and Python.

    “It’s been very important to sell solutions, not building blocks,” Matz says. “A big part of our success in the life sciences with top pharmas and biotechs and research institutes is bringing them our REVEAL suite of application-specific solutions to problems. We’re not handing them an analytical platform that’s a set of LEGO blocks; we’re giving them solutions that handle the data they deal with daily, and solutions that use their vocabulary and answer the questions they want to work on.”

    Accelerating discovery

    Today Paradigm4’s customers include some of the biggest pharmaceutical and biotech companies in the world as well as research labs at the National Institutes of Health, Stanford University, and elsewhere.

    Customers can integrate genomic sequencing data, biometric measurements, data on environmental factors, and more into their inquiries to enable new discoveries across a range of life science fields.

    Matz says SciDB did 1 billion linear regressions in less than an hour in a recent benchmark, and that it can scale well beyond that, which could speed up discoveries and lower costs for researchers who have traditionally had to extract their data from files and then rely on less efficient cloud-computing-based methods to apply algorithms at scale.

    “If researchers can run complex analytics in minutes and that used to take days, that dramatically changes the number of hard questions you can ask and answer,” Matz says. “That is a force-multiplier that will transform research daily.”

    Beyond life sciences, Paradigm4’s system holds promise for any industry dealing with multifaceted data, including earth sciences, where Matz says a NASA climatologist is already using the system, and industrial IoT, where data scientists consider large amounts of diverse data to understand complex manufacturing systems. Matz says the company will focus more on those industries next year.

    In the life sciences, however, the founders believe they already have a revolutionary product that’s enabling a new world of discoveries. Down the line, they see SciDB and REVEAL contributing to national and worldwide health research that will allow doctors to provide the most informed, personalized care imaginable.

    “The query that every doctor wants to run is, when you come into his or her office and display a set of symptoms, the doctor asks, ‘Who in this national database has genetics that look like mine, symptoms that look like mine, lifestyle exposures that look like mine? And what was their diagnosis? What was their treatment? And what was their morbidity?” Stonebraker explains. “This is cross correlating you with everybody else to do very personalized medicine, and I think this is within our grasp.”

    See the full article here .


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    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

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  • richardmitnick 8:51 am on April 2, 2020 Permalink | Reply
    Tags: "MIT initiates mass manufacture of disposable face shields for Covid-19 response", , , MIT   

    From MIT News: “MIT initiates mass manufacture of disposable face shields for Covid-19 response” 

    MIT News

    From MIT News

    March 31, 2020
    Mary Beth Gallagher | Department of Mechanical Engineering

    1
    Robyn Goodner, who serves as a maker technical specialist for Project Manus, models the face shield design in the Metropolis Makerspace. Image: Project Manus

    2
    Elazer Edelman, the Edward J. Poitras Professor in Medical Engineering and Science at MIT, wears a face shield developed through a collaborative effort involving groups across MIT, while holding an electronic, Bluetooth-enabled stethoscope. In this photo, Edelman is wearing the shield in the snapped up position health care workers also have the option to use. Image: Elazer Edelman

    A team from MIT has designed disposable face shields that can be mass produced quickly to address hospitals’ needs nationwide.

    The shortage of personal protective equipment (PPE) available to health care professionals has become increasingly problematic as Covid-19 cases continue to surge. The sheer volume of PPE needed to keep doctors, nurses, and their patients safe in this crisis is daunting — for example, tens of millions of disposable face shields will be needed nationwide each month. This week, a team from MIT launched mass manufacturing of a new technique to meet the high demand for disposable face shields.

    The single piece face shield design will be made using a process known as die cutting. Machines will cut the design from thousands of flat sheets per hour. Once boxes of these flat sheets arrive at hospitals, health care professionals can quickly fold them into three-dimensional face shields before adjusting for their faces.

    “These face shields have to be made rapidly and at low cost because they need to be disposable,” explains Martin Culpepper, professor of mechanical engineering, director of Project Manus, and a member of MIT’s governance team on manufacturing opportunities for Covid-19. “Our technique combines low-cost materials with a high-rate manufacturing that has the potential of meeting the need for face shields nationwide.”

    Culpepper and his team at Project Manus spearheaded the development of the technique in collaboration with a number of partners from MIT, local-area hospitals, and industry. The team has been working closely with the MIT Medical Outreach team and the Crisis Management Unit established by Vice President for Research Maria Zuber and directed by Elazer R. Edelman, the Edward J. Poitras Professor in Medical Engineering and Science at MIT.

    Extending the life of face masks

    When used correctly, face masks should be changed every time a doctor or nurse treats a new patient. However, over the past month, many health care professionals have been asked to wear one face mask per day. That one mask could carry virus particles — potentially contributing to the spread of Covid-19 within hospitals and endangering health care professionals.

    “The lack of adequate protective equipment or the idea of reusing potentially contaminated equipment is especially frightening to health care workers who are putting their lives, and by extension the lives and well-being of their families, on the line every day,” explains Edelman, who is also the director of MIT’s Institute for Medical Engineering and Science (IMES) and leader of MIT’s PPE task force.

    Face shields can address this problem by providing another layer of protection that covers masks and entire faces while extending the life of PPE. The shields are made of clear materials and have a shape similar to a welder’s mask. They protect the health care professional and their face mask from coming in direct contact with virus particles spread through coughing or sneezing.

    “If we can slow down the rate at which health care professionals use face masks with a disposable face shield, we can make a real difference in protecting their health and safety,” explains Culpepper.

    Culpepper and his team at Project Manus set out to design a face shield that could be rapidly produced at a scale large enough to meet the growing demand. They landed on a flat design that people could quickly fold into a three dimensional structure when the shield was ready for use. Their design also includes extra protection with flaps that fold under the neck and over the forehead.

    As much of MIT’s campus came to a halt in light of social distancing measures being put in place, Culpepper started prototyping using a laser cutter he had in his house. Along with some design input from his children, he tested different materials and made the first 10 prototypes at home.

    “When you’re thinking of materials, you have to keep supply chains in mind. You can’t choose a material that could evaporate from the supply chain. That is a challenging problem in this crisis,” explains Culpepper. After testing a few materials that cracked and broke when bent, the team chose polycarbonate and polyethylene terephthalate glycol – known more commonly as PETG – as the shield’s material.

    In addition to making more prototypes at the Project Manus Metropolis Makerspace using a laser cutter, Culpepper worked with Professor Neil Gershenfeld and his team at MIT’s Center for Bits and Atoms (CBA) on rapid-prototyping designs for testing using a Zund large-format cutter.

    Gershenfeld’s team at CBA is working on a number of projects for coronavirus response using its digital fabrication facility at MIT as well as the global Fab Lab network it launched. “The coronavirus response site is a great resource for those that are interested in working on solutions for PPE and devices for the Covid-19 pandemic,” Culpepper adds.

    “It’s been a pleasure in this difficult time collaborating with such an impressive group, drawing on all of the Institute’s strengths to quickly define and refine a solution to an urgent need,” says Gershenfeld. “The work at MIT will be valuable beyond its immediate local impact, as a best-practices reference for the many other face shield projects emerging around the world.”

    Testing the shield at local hospitals

    With a number of working prototypes built, Culpepper and his team moved to the testing phase after consultation with, and practical feedback from, Edelman, who is also a physician.

    “The single greatest insecurity of a health care provider is the thought that we will become infected and in doing so be unable to perform our duties or infect others,” adds Edelman.

    Edelman demonstrated how to store, assemble, and use the face shields for nurses and physicians at a number of area hospitals. Participants were then asked to use them in real-life situations and provide feedback using a one-page survey.

    The feedback was overwhelmingly positive — participants found that in addition to being easy to assemble and use, the MIT-designed shields provided good protection against coming in contact with virus particles through splashes or aerosolized particles.

    Armed with this feedback, Culpepper’s team made a few minor adjustments to the design to maximize coverage around the sides and neck of users. With the design finalized, the project has this week shifted to high-rate mass manufacturing.

    High-rate mass manufacturing

    The die cutter machines used in mass manufacturing will produce the flat face shields at a rate of 50,000 shields per day in a few weeks. The manufacturer will continue to ramp up and increase the rate of manufacturing further with the ability to fabricate in more than 80 facilities nationwide.

    “This process has been designed in such a way that there is the potential to ramp up to millions of face shields produced per day,” explains Culpepper. “This could very quickly become a nationwide solution for face shield shortages.”

    MIT plans on purchasing the first 40,000 face shields to donate to local Boston-area hospitals this week and the fabrication facilities will donate 60,000.

    “Having an adequate and perhaps even endless supply of PPE is absolutely critical to ensuring the safety of the entire population, especially those who care for Covid-19 patients,” adds Edelman.

    Throughout the process, Culpepper’s team received help from a number of colleagues and departments across MIT. This includes MIT’s Office of the Vice President for Research, Professor Elazer Edelman, Tolga Durak, managing director of the MIT Environment, Health and Safety Office, the Center for Bits and Atoms, MIT Procurement Operations, MIT’s Office of the General Counsel, MIT’s Department of Mechanical Engineering, and colleagues from MIT Lincoln Laboratory, who helped source material to build the face shields and supported design iterations. They also received advice from MIT colleagues working with the Massachusetts Technology Collaborative, which is helping organize manufacturers for Covid-19 response.

    “This project was a great example of collaboration across MIT and the employment of mind-heart-hand. When we reached out to others, they dropped everything to put their minds and hands to work helping us make this happen quickly,” says Culpepper. “It is also a great example for others to look to safely and rapidly innovate PPE for Covid-19.”

    See the full article here .


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    Please help promote STEM in your local schools.


    Stem Education Coalition

    MIT Seal

    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

    MIT Campus

     
  • richardmitnick 10:19 am on April 1, 2020 Permalink | Reply
    Tags: MIT, The data speak: Stronger pandemic response yields better economic recovery"   

    From MIT News: “The data speak: Stronger pandemic response yields better economic recovery” 

    MIT News

    From MIT News

    March 31, 2020
    Peter Dizikes

    1
    A new study co-authored by Emil Verner, an assistant professor at The MIT Sloan School of Management, shows that in the 1918 flu pandemic, cities that had more aggressive interventions including social distancing also experienced stronger economic recoveries afterward. Image: Christine Daniloff, MIT; stock image buildings.

    Study of 1918 flu pandemic shows U.S. cities that responded more aggressively in health terms also had better economic rebounds.

    The research described in this article has been published as a working paper but has not yet been peer-reviewed by experts in the field.

    With much of the U.S. in shutdown mode to limit the spread of the Covid-19 disease, a debate has sprung up about when the country might “reopen” commerce, to limit economic fallout from the pandemic. But as a new study co-authored by an MIT economist shows, taking care of public health first is precisely what generates a stronger economic rebound later.

    The study, using data from the flu pandemic that swept the U.S. in 1918-1919, finds cities that acted more emphatically to limit social and civic interactions had more economic growth following the period of restrictions.

    Indeed, cities that implemented social-distancing and other public health interventions just 10 days earlier than their counterparts saw a 5 percent relative increase in manufacturing employment after the pandemic ended, through 1923. Similarly, an extra 50 days of social distancing was worth a 6.5 percent increase in manufacturing employment, in a given city.

    “We find no evidence that cities that acted more aggressively in public health terms performed worse in economic terms,” says Emil Verner, an assistant professor in the MIT Sloan School of Management and co-author of a new paper detailing the findings. “If anything, the cities that acted more aggressively performed better.”

    With that in mind, he observes, the idea of a “trade-off” between public health and economic activity does not hold up to scrutiny; places that are harder hit by a pandemic are unlikely to rebuild their economic capacities as quickly, compared to areas that are more intact.

    “It casts doubt on the idea there is a trade-off between addressing the impact of the virus, on the one hand, and economic activity, on the other hand, because the pandemic itself is so destructive for the economy,” Verner says.

    The study, “Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu,” was posted to the Social Science Research Network as a working paper on March 26. In addition to Verner, the co-authors are Sergio Correia, an economist with the U.S. Federal Reserve, and Stephen Luck, an economist with the Federal Reserve Bank of New York.

    Evaluating economic consequences

    To conduct the research, the three scholars examined mortality statistics from the U.S. Centers for Disease Control (CDC), historical economic data from the U.S. Census Bureau, and banking statistics compiled by finance economist Mark D. Flood, using the “Annual Reports of the Comptroller of Currency,” a government publication.

    As Verner notes, the researchers were motivated to investigate the 1918-1919 flu pandemic to see what lessons from it might be applicable to the current crisis.

    “The genesis of the study is that we’re interested in what the expected economic impacts of today’s coronavirus are going to be, and what is the right way to think about the economic consequences of the public health and social distancing interventions we’re seeing all around the world,” Verner says.

    Scholars have known that the varying use of “nonpharmaceutical interventions,” or social-distancing measures, correlated to varying health outcomes across cities in 1918 and 1919. When that pandemic hit, U.S. cities that shut down schools earlier, such as St. Louis, fared better against the flu than places implementing shutdowns later, such as Philadelphia. The current study extends that framework to economic activity.

    Quite a bit like today, social distancing measures back then included school and theater closures, bans on public gatherings, and restricted business activity.

    “The nonpharmaceutical interventions that were implemented in 1918 interestingly resemble many of the policies that are being used today to reduce the spread of Covid-19,” Verner says.

    Overall, the study indicates, the economic impact of the pandemic was severe. Using state-level data, the researchers find an 18 percent drop in manufacturing output through 1923, well after the last wave of the flu hit in 1919.

    Looking at the effect across 43 cities, however, the researchers found significantly different economic outcomes, linked to different social distancing policies. The best-performing cities included Oakland, California; Omaha, Nebraska; Portland, Oregon; and Seattle, which all enforced over 120 days of social distancing in 1918. Cities that instituted fewer than 60 days of social distancing in 1918, and saw manufacturing struggle afterward, include Philadelphia; St. Paul, Minnesota; and Lowell, Massachusetts.

    “What we find is that areas that were more severely affected in the 1918 flu pandemic see a sharp and persistent decline in a number of measures of economic activity, including manufacturing employment, manufacturing output, bank loans, and the stock of consumer durables,” Verner says.

    Banking issues

    As far as banking goes, the study included banking write-downs as an indicator of economic health, because “banks were recognizing losses from loans that households and businesses were defaulting on, due to the economic disruption caused by the pandemic,” Verner says.

    The researchers found that in Albany, New York; Birmingham, Alabama; Boston; and Syracuse, New York — all of which also had fewer than 60 days of social distancing in 1918 — the banking sector struggled more than anywhere else in the country.

    As the authors note in the paper, the economic struggles that followed the 1918-1919 flu pandemic reduced the ability of firms to manufacture goods — but the reduction in employment meant that people had less purchasing power as well.

    “The evidence that we have in our paper … suggests that the pandemic creates both a supply-side problem and a demand-side problem,” Verner notes.

    As Verner readily acknowledges, the composition of the U.S. economy has evolved since 1918-1919, with relatively less manufacturing today and relatively more activity in services. The 1918-1919 pandemic was also especially deadly for prime working-age adults, making its economic impact particularly severe. Still, the economists think the dynamics of the previous pandemic are readily applicable to our ongoing crisis.

    “The structure of the economy is of course different,” Verner notes. However, he adds, “While one shouldn’t extrapolate too directly from history, we can learn some of the lessons that may be relevant to us today.” First among those lessons, he emphasizes: “Pandemic economics are different than normal economics.”

    See the full article here .


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    Please help promote STEM in your local schools.


    Stem Education Coalition

    MIT Seal

    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

    MIT Campus

     
  • richardmitnick 4:05 pm on March 30, 2020 Permalink | Reply
    Tags: A team of MIT chemists has designed a drug candidate that they believe may block coronaviruses’ ability to enter human cells., “We’ve built these platforms for really rapid turnaround"., MIT, They are now developing about 100 different variants of the peptide in hopes of increasing its binding strength and making it more stable in the body.   

    From MIT News: “An experimental peptide could block Covid-19” 

    MIT News

    From MIT News

    March 27, 2020
    Anne Trafton

    1
    MIT chemists have designed a peptide that can bind to part of the coronavirus spike protein, which they hope may prevent the virus from being able to enter cells. Photo collage: Christine Daniloff, MIT.

    The research described in this article has been published on a preprint server but has not yet been peer-reviewed by scientific or medical experts.

    In hopes of developing a possible treatment for Covid-19, a team of MIT chemists has designed a drug candidate that they believe may block coronaviruses’ ability to enter human cells. The potential drug is a short protein fragment, or peptide, that mimics a protein found on the surface of human cells.

    The researchers have shown that their new peptide can bind to the viral protein that coronaviruses use to enter human cells, potentially disarming it.

    “We have a lead compound that we really want to explore, because it does, in fact, interact with a viral protein in the way that we predicted it to interact, so it has a chance of inhibiting viral entry into a host cell,” says Brad Pentelute, an MIT associate professor of chemistry, who is leading the research team.

    The MIT team reported its initial findings in a preprint posted on bioRxiv, an online preprint server, on March 20. They have sent samples of the peptide to collaborators who plan to carry out tests in human cells.

    Molecular targeting

    Pentelute’s lab began working on this project in early March, after the Cryo-EM structure of the coronavirus spike protein, along with the human cell receptor that it binds to, was published [Science]by a research group in China. Coronaviruses, including SARS-CoV-2, which is causing the current Covid-19 outbreak, have many protein spikes protruding from their viral envelope.

    Studies of SARS-CoV-2 have also shown that a specific region of the spike protein, known as the receptor binding domain, binds to a receptor called angiotensin-converting enzyme 2 (ACE2). This receptor is found on the surface of many human cells, including those in the lungs. The ACE2 receptor is also the entry point used by the coronavirus that caused the 2002-03 SARS outbreak.

    In hopes of developing drugs that could block viral entry, Genwei Zhang, a postdoc in Pentelute’s lab, performed computational simulations of the interactions between the ACE2 receptor and the receptor binding domain of the coronavirus spike protein. These simulations revealed the location where the receptor binding domain attaches to the ACE2 receptor — a stretch of the ACE2 protein that forms a structure called an alpha helix.

    “This kind of simulation can give us views of how atoms and biomolecules interact with each other, and which parts are essential for this interaction,” Zhang says. “Molecular dynamics helps us narrow down particular regions that we want to focus on to develop therapeutics.”

    The MIT team then used peptide synthesis technology that Pentelute’s lab has previously developed, to rapidly generate a 23-amino acid peptide with the same sequence as the alpha helix of the ACE2 receptor. Their benchtop flow-based peptide synthesis machine can form linkages between amino acids, the buildings blocks of proteins, in about 37 seconds, and it takes less than an hour to generate complete peptide molecules containing up to 50 amino acids.

    “We’ve built these platforms for really rapid turnaround, so I think that’s why we’re at this point right now,” Pentelute says. “It’s because we have these tools we’ve built up at MIT over the years.”

    They also synthesized a shorter sequence of only 12 amino acids found in the alpha helix, and then tested both of the peptides using equipment at MIT’s Biophysical Instrumentation Facility that can measure how strongly two molecules bind together. They found that the longer peptide showed strong binding to the receptor binding domain of the Covid-19 spike protein, while the shorter one showed negligible binding.

    Many variants

    Although MIT has been scaling back on-campus research since mid-March, Pentelute’s lab was granted special permission allowing a small group of researchers to continue to work on this project. They are now developing about 100 different variants of the peptide in hopes of increasing its binding strength and making it more stable in the body.

    “We have confidence that we know exactly where this molecule is interacting, and we can use that information to further guide refinement, so that we can hopefully get a higher affinity and more potency to block viral entry in cells,” Pentelute says.

    In the meantime, the researchers have already sent their original 23-amino acid peptide to a research lab at the Icahn School of Medicine at Mount Sinai for testing in human cells and potentially in animal models of Covid-19 infection.

    While dozens of research groups around the world are using a variety of approaches to seek new treatments for Covid-19, Pentelute believes his lab is one of a few currently working on peptide drugs for this purpose. One advantage of such drugs is that they are relatively easy to manufacture in large quantities. They also have a larger surface area than small-molecule drugs.

    “Peptides are larger molecules, so they can really grip onto the coronavirus and inhibit entry into cells, whereas if you used a small molecule, it’s difficult to block that entire area that the virus is using,” Pentelute says. “Antibodies also have a large surface area, so those might also prove useful. Those just take longer to manufacture and discover.”

    One drawback of peptide drugs is that they typically can’t be taken orally, so they would have to be either administered intravenously or injected under the skin. They would also need to be modified so that they can stay in the bloodstream long enough to be effective, which Pentelute’s lab is also working on.

    “It’s hard to project how long it will take to have something we can test in patients, but my aim is to have something within a matter of weeks. If it turns out to be more challenging, it may take months,” he says.

    In addition to Pentelute and Zhang, other researchers listed as authors on the preprint are postdoc Sebastian Pomplun, grad student Alexander Loftis, and research scientist Andrei Loas.

    See the full article here .


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    Please help promote STEM in your local schools.


    Stem Education Coalition

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    The mission of MIT is to advance knowledge and educate students in science, technology, and other areas of scholarship that will best serve the nation and the world in the twenty-first century. We seek to develop in each member of the MIT community the ability and passion to work wisely, creatively, and effectively for the betterment of humankind.

    MIT Campus

     
    • Skyscapes for the Soul 5:50 pm on March 30, 2020 Permalink | Reply

      So there is the chance that this all will be over and I have to get back to shows and galleries and Census work before I’ve had time to catch up on the deferred maintenance around the house, repainted the interior, done a deep cleaning, perfected my ability to cut with a Skilsaw, finished all the yard work, read all the books on the shelf, had long phone calls with people I love but rarely have chance to talk too, painted Ken’s house inside and out, removed extraneous cabinets in the laundry room……. I knew there was a reason I felt rushed…. (sorry my sense of humor is actually more warped than the USS Enterprise….)

      Like

  • richardmitnick 12:23 pm on March 27, 2020 Permalink | Reply
    Tags: "MIT-based team works on rapid deployment of open-source low-cost ventilator", , “At present we are awaiting FDA feedback” about the project. “Ultimately our intent is to seek FDA approval. That process takes time however.”, Covid-19 pandemic, , MIT, The innovation now being rapidly refined and tested by the new team was to devise a mechanical system to do the squeezing and releasing of the Ambu bag., The team started with a hand-operated plastic pouch called a bag-valve resuscitator or Ambu bag which hospitals already have on hand in large quantities.   

    From MIT News: “MIT-based team works on rapid deployment of open-source, low-cost ventilator” 

    MIT News

    From MIT News

    March 26, 2020
    David L. Chandler

    Clinical and design considerations will be published online; goal is to support rapid scale-up of device production to alleviate hospital shortages.

    1
    The new device fits around an Ambu bag (blue), which hospitals already have on hand in abundance. Designed to be squeezed by hand, instead they are squeezed by mechanical paddles (center) driven by a small motor. This directs air through a tube which is placed in the patient’s airway. Images: courtesy of the researchers.

    2
    This shows the setup used for preliminary testing of an earlier version of the low-cost prototype design that could provide rapid deployment to hospitals facing shortages of the vital equipment Images: courtesy of the researchers.

    3
    Test setup in the lab shows the most recent version of the device undergoing initial testing.Images: courtesy of the researchers.

    One of the most pressing shortages facing hospitals during the Covid-19 emergency is a lack of ventilators. These machines can keep patients breathing when they no longer can on their own, and they can cost around $30,000 each. Now, a rapidly assembled volunteer team of engineers, physicians, computer scientists, and others, centered at MIT, is working to implement a safe, inexpensive alternative for emergency use, which could be built quickly around the world.

    The team, called MIT E-Vent (for emergency ventilator), was formed on March 12 in response to the rapid spread of the Covid-19 pandemic. Its members were brought together by the exhortations of doctors, friends, and a sudden flood of mail referencing a project done a decade ago in the MIT class 2.75 (Medical Device Design). Students working in consultation with local physicians designed a simple ventilator device that could be built with about $100 worth of parts. They published a paper detailing their design and testing, but the work ended at that point. Now, with a significant global need looming, a new team, linked to that course, has resumed the project at a highly accelerated pace.

    The team, called MIT E-Vent (for emergency ventilator), was formed on March 12 in response to the rapid spread of the Covid-19 pandemic. Its members were brought together by the exhortations of doctors, friends, and a sudden flood of mail referencing a project done a decade ago in the MIT class 2.75 (Medical Device Design). Students working in consultation with local physicians designed a simple ventilator device that could be built with about $100 worth of parts. They published a paper detailing their design and testing, but the work ended at that point. Now, with a significant global need looming, a new team, linked to that course, has resumed the project at a highly accelerated pace.

    The key to the simple, inexpensive ventilator alternative is a hand-operated plastic pouch called a bag-valve resuscitator, or Ambu bag, which hospitals already have on hand in large quantities. These are designed to be operated by hand, by a medical professional or emergency technician, to provide breaths to a patient in situations like cardiac arrest, until an intervention such as a ventilator becomes available. A tube is inserted into the patient’s airway, as with a hospital ventilator, but then the pumping of air into the lungs is done by squeezing and releasing the flexible pouch. This is a task for skilled personnel, trained in how to evaluate the patient and adjust the timing and pressure of the pumping accordingly.

    The innovation begun by the earlier MIT class, and now being rapidly refined and tested by the new team, was to devise a mechanical system to do the squeezing and releasing of the Ambu bag, since this is not something that a person could be expected to do for any extended period. But it is crucial for such a system to not damage the bag and to be controllable, so that the amount of air and pressures being delivered can be tailored to the particular patient. The device must be very reliable, since an unexpected failure of the device could be fatal, but as designed by the MIT team, the bag can be immediately operated manually.

    The team is particularly concerned about the potential for well-meaning but inexperienced do-it-yourselfers to try to reproduce such a system without the necessary clinical knowledge or expertise with hardware that can operate for days; around 1 million cycles would be required to support a ventilated patient over a two-week period. Furthermore, it requires code that is fault-tolerant, since ventilators are precision devices that perform a life-critical function. To help curtail the spread of misinformation or poorly-thought-out advice, the team has added to their website verified information resources on the clinical use of ventilators and the requirements for training and monitoring in using such systems. All of this information is freely available at e-vent.mit.edu.

    “We are releasing design guidance (clinical, mechanical, electrical/controls, testing) on a rolling basis as it is developed and documented,” one team member says. “We encourage capable clinical-engineering teams to work with their local resources, while following the main specs and safety information, and we welcome any input other teams may have.”

    The researchers emphasize that this is not a project for typical do-it-yourselfers to undertake, since it requires specialized understanding of the clinical-technical interface, and the ability to work in consideration of strict U.S. Food and Drug Administration specifications and guidelines.

    Such devices “have to be manufactured according to FDA requirements, and should only be utilized under the supervision of a clinician,” a team member said. “The Department of Health and Human Services released a notice stating that all medical interventions related to Covid-19 are no longer subject to liability, but that does not change our burden of care.” he said. “At present, we are awaiting FDA feedback” about the project. “Ultimately, our intent is to seek FDA approval. That process takes time, however.”

    The all-volunteer team is working without funding and operating anonymously for now because many of them have already been swamped by inquiries from people wanting more information, and are concerned about being overwhelmed by calls that would interfere with their work on the project. “We would really, really like to just stay focused,” says one team member. “And that’s one of the reasons why the website is so essential, so that we can communicate with anyone who wants to read about what we are doing, and also so that others across the world can communicate with us.”

    “The primary consideration is patient safety. So we had to establish what we’re calling minimum clinical functional requirements,” that is, the minimum set of functions that the device would need to perform to be both safe and useful, says one of the team members, who is both an engineer and an MD. He says one of his jobs is to translate between the specialized languages used by the engineers and the medical professionals on the team.

    That determination of minimum requirements was made by a team of physicians with broad clinical backgrounds, including anesthesia and critical care, he says. In parallel, the group set to work on designing, building, and testing an updated prototype. Initial tests revealed the high loads that actual use incurs, and some weaknesses that have already been addressed so that, in the words of team co-leads, “Even the professor can kick it across the room.” In other words, early attempts focused on super “makability” were too optimistic.

    New versions have already been fabricated and are being prepared for additional functional tests. Already, the team says there is enough detailed information on their website to allow other teams to work in parallel with them, and they have also included links to other teams that are working on similar design efforts.

    In under a week the team has gone from empty benches to their first realistic tests of a prototype. One team member says that in the less than a week full they have been working, motivated by reports of doctors already having to ration ventilators, and the intense focus the diverse group has brought to this project, they have already generated “multiple theses worth” of research.

    The cross-disciplinary nature of the group has been crucial, one team member says. “The most exciting times and when the team is really moving fast are when we have an a design engineer, sitting next to a controls engineer, sitting next to the fabrication expert, with an anesthesiologist on WebEx, all solid modeling, coding, and spreadsheeting in parallel. We are discussing the details of everything from ways to track patients’ vital signs data to the best sources for small electric motors.”

    The intensity of the work, with people putting in very long hours every day, has been tiring but hasn’t dulled their enthusiasm. “We all work together, and ultimately the goal is to help people, because people’s lives understandably hang in the balance,” he said.

    The team can be contacted via their website.

    See the full article here .


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  • richardmitnick 12:33 pm on March 24, 2020 Permalink | Reply
    Tags: , Covid-19 High Performance Computing Consortium, , MIT,   

    From MIT News: “MIT joins White House supercomputing effort to speed up search for Covid-19 solutions” 

    MIT News

    From MIT News

    March 23, 2020
    Jennifer Chu

    1
    MIT joins a consortium of supercomputing resources to help speed the search for Covid-19 solutions. Image: CDC, MIT News.

    The White House has announced the launch of the Covid-19 High Performance Computing Consortium, a collaboration among various industry, government, and academic institutions which will aim to make their supercomputing resources available to the wider research community, in an effort to speed up the search for solutions to the evolving Covid-19 pandemic.

    MIT has joined the consortium, which is led by the U.S. Department of Energy, the National Science Foundation, and NASA.

    MIT News spoke with Christopher Hill, principal research scientist in MIT’s Department of Earth, Atmospheric, and Planetary Sciences, who is serving on the new consortium’s steering committee, about how MIT’s computing power will aid in the fight against Covid-19.

    Q: How did MIT become a part of this consortium?

    A: IBM, which has longstanding computing relationships with both the government and MIT, approached the Institute late last week about joining. The Department of Energy owns IBM’s Summit supercomputer, located at Oak Ridge National Laboratory, which was already working on finding pharmaceutical compounds that might be effective against this coronavirus. In addition to its close working relationship with MIT, IBM also had donated the Satori supercomputer as part of the launch of the MIT Schwarzman College of Computing. We obviously want to do everything we can to help combat this pandemic, so we jumped at the chance to be part of a larger effort.

    Q: What is MIT bringing to the consortium?

    A: We’re primarily bringing two systems to the effort: Satori and Supercloud, which is an unclassified system run by Lincoln Laboratory. Both systems have very large numbers of the computing units — known as GPUs — that enable the machines to process information far more quickly, and they also have extra large memory. That makes the systems slightly different from other machines in the consortium in ways that may be helpful for some types of problems.

    For example, MIT’s two systems seem to be especially helpful at examining images from cryo-electron microscopy, which entails use of an electron microscope on materials at ultralow temperatures. Ultralow temperatures slow the motion of atoms, making the images clearer. In addition to the hardware, MIT faculty and staff have already expressed interest in assisting outside researchers who are using MIT equipment.

    Q: How will MIT operate as part of the consortium?

    A: The consortium will receive proposals through a single portal being run in conjunction with the NSF. A steering committee will decide which proposals are accepted and where to route them. The steering committee will be relying on guidance from a larger technical review committee, which will include the steering committee members and additional experts. Both committees are made of researchers from the participating institutions. I will serve on both committees for MIT, and we’ll be appointing a second person to serve on the technical review committee.

    Four individuals at MIT — Ben Forget, Nick Roy, Jeremy Kepner (Lincoln Lab), and myself ­— will oversee the work at the Institute. The goal of the consortium is to focus on projects where computing is likely to produce relevant advances in one week to three months —though some projects, like those related to vaccines — may take longer.

    See the full article here .


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  • richardmitnick 12:48 pm on March 12, 2020 Permalink | Reply
    Tags: , , Magnetite as an insulator via Verwey transition., MIT, , The researchers suggest that the larger significance of this finding will impact the field of fundamental condensed matter physics., This discovery is significant because no frozen waves of any kind had ever been found in magnetite., This work led by MIT professor of physics Nuh Gedik was made possible by the use of “ultrafast terahertz spectroscopy.   

    From MIT News: “Dancing electrons solve a longstanding puzzle in the oldest magnetic material” 

    MIT News

    From MIT News

    March 11, 2020
    Sandi Miller | Department of Physics

    1
    Researchers confirmed the existence of electronic waves that are frozen at a transition temperature of 125 kelvins and start “dancing together” in a collective oscillating motion as the temperature is lowered. In this illustration, a red laser beam triggers the dance of the newly discovered electronic waves in magnetite. Image: Ambra Garlaschelli.

    2
    Edoardo Baldini (left) and Carina Belvin work in the Gedik lab at MIT. The researchers used ultrafast lasers in the extreme infrared for their magnetite discovery. Photo courtesy of the Department of Physics.

    Physicists use extreme infrared laser pulses to reveal frozen electron waves in magnetite.

    Magnetite is the oldest magnetic material known to humans, yet researchers are still mystified by certain aspects of its properties.

    For example, when the temperature is lowered below 125 kelvins, magnetite changes from a metal to an insulator, its atoms shift to a new lattice structure, and its charges form a complicated ordered pattern. This extraordinarily complex phase transformation, which was discovered in the 1940s and is known as the Verwey transition, was the first metal-insulator transition ever observed. For decades, researchers have not understood exactly how this phase transformation was happening.

    According to a paper published March 9 in Nature Physics, an international team of experimental and theoretical researchers discovered fingerprints of the quasiparticles that drive the Verwey transition in magnetite. Using an ultrashort laser pulse, the researchers were able to confirm the existence of peculiar electronic waves that are frozen at the transition temperature and start “dancing together” in a collective oscillating motion as the temperature is lowered.

    “We were investigating the mechanism behind the Verwey transition and we suddenly found anomalous waves freezing at the transition temperature” said MIT physics postdoc Edoardo Baldini, one of the lead authors on the paper. “They are waves made of electrons that displace the surrounding atoms and move collectively as fluctuations in space and time.”

    This discovery is significant because no frozen waves of any kind had ever been found in magnetite. “We immediately understood that these were interesting objects that conspire in triggering this very complex phase transition,” says MIT physics PhD student Carina Belvin, the paper’s other lead author.

    These objects that form the low-temperature charge order in magnetite are “trimerons,” three-atom building blocks. “By performing an advanced theoretical analysis, we were able to determine that the waves we observed correspond to the trimerons sliding back and forth,” explains Belvin.

    “The understanding of quantum materials such as magnetite is still in its infancy because of the extremely complex nature of the interactions that create exotic ordered phases,” adds Baldini.

    The researchers suggest that the larger significance of this finding will impact the field of fundamental condensed matter physics, advancing the comprehension of a conceptual puzzle that has been open since the early 1940s. This work, led by MIT professor of physics Nuh Gedik, was made possible by the use of “ultrafast terahertz spectroscopy,” an advanced laser apparatus based on ultrashort pulses in the extreme infrared. Gedik says, “These laser pulses are as short as one millionth of one millionth of a second and allow us to take fast photographs of the microscopic world. Our goal now is to apply this approach to discover new classes of collective waves in other quantum materials.”

    See the full article here .


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  • richardmitnick 12:59 pm on March 11, 2020 Permalink | Reply
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    From MIT News: “Novel method for easier scaling of quantum devices” 

    MIT News

    From MIT News

    March 5, 2020
    Rob Matheson

    1
    An MIT team found a way to “recruit” normally disruptive quantum bits (qubits) in diamond to, instead, help carry out quantum operations. This approach could be used to help scale up quantum computing systems. Image: Christine Daniloff, MIT.

    System “recruits” defects that usually cause disruptions, using them to instead carry out quantum operations.

    In an advance that may help researchers scale up quantum devices, an MIT team has developed a method to “recruit” neighboring quantum bits made of nanoscale defects in diamond, so that instead of causing disruptions they help carry out quantum operations.

    Quantum devices perform operations using quantum bits, called “qubits,” that can represent the two states corresponding to classic binary bits — a 0 or 1 — or a “quantum superposition” of both states simultaneously. The unique superposition state can enable quantum computers to solve problems that are practically impossible for classical computers, potentially spurring breakthroughs in biosensing, neuroimaging, machine learning, and other applications.

    One promising qubit candidate is a defect in diamond, called a nitrogen-vacancy (NV) center, which holds electrons that can be manipulated by light and microwaves. In response, the defect emits photons that can carry quantum information. Because of their solid-state environments, however, NV centers are always surrounded by many other unknown defects with different spin properties, called “spin defects.” When the measurable NV-center qubit interacts with those spin defects, the qubit loses its coherent quantum state — “decoheres”— and operations fall apart. Traditional solutions try to identify these disrupting defects to protect the qubit from them.

    In a paper published Feb. 25 in Physical Review Letters, the researchers describe a method that uses an NV center to probe its environment and uncover the existence of several nearby spin defects. Then, the researchers can pinpoint the defects’ locations and control them to achieve a coherent quantum state — essentially leveraging them as additional qubits.

    In experiments, the team generated and detected quantum coherence among three electronic spins — scaling up the size of the quantum system from a single qubit (the NV center) to three qubits (adding two nearby spin defects). The findings demonstrate a step forward in scaling up quantum devices using NV centers, the researchers say.

    “You always have unknown spin defects in the environment that interact with an NV center. We say, ‘Let’s not ignore these spin defects, which [if left alone] could cause faster decoherence. Let’s learn about them, characterize their spins, learn to control them, and ‘recruit’ them to be part of the quantum system,’” says the lead co-author Won Kyu Calvin Sun, a graduate student in the Department of Nuclear Science and Engineering and a member of the Quantum Engineering group. “Then, instead of using a single NV center [or just] one qubit, we can then use two, three, or four qubits.”

    Joining Sun on the paper are lead author Alexandre Cooper ’16 of Caltech; Jean-Christophe Jaskula, a research scientist in the MIT Research Laboratory of Electronics (RLE) and member of the Quantum Engineering group at MIT; and Paola Cappellaro, a professor in the Department of Nuclear Science and Engineering, a member of RLE, and head of the Quantum Engineering group at MIT.

    Characterizing defects

    NV centers occur where carbon atoms in two adjacent places in a diamond’s lattice structure are missing — one atom is replaced by a nitrogen atom, and the other space is an empty “vacancy.” The NV center essentially functions as an atom, with a nucleus and surrounding electrons that are extremely sensitive to tiny variations in surrounding electrical, magnetic, and optical fields. Sweeping microwaves across the center, for instance, makes it change, and thus control, the spin states of the nucleus and electrons.

    Spins are measured using a type of magnetic resonance spectroscopy. This method plots the frequencies of electron and nucleus spins in megahertz as a “resonance spectrum” that can dip and spike, like a heart monitor. Spins of an NV center under certain conditions are well-known. But the surrounding spin defects are unknown and difficult to characterize.

    In their work, the researchers identified, located, and controlled two electron-nuclear spin defects near an NV center. They first sent microwave pulses at specific frequencies to control the NV center. Simultaneously, they pulse another microwave that probes the surrounding environment for other spins. They then observed the resonance spectrum of the spin defects interacting with the NV center.

    The spectrum dipped in several spots when the probing pulse interacted with nearby electron-nuclear spins, indicating their presence. The researchers then swept a magnetic field across the area at different orientations. For each orientation, the defect would “spin” at different energies, causing different dips in the spectrum. Basically, this allowed them to measure each defect’s spin in relation to each magnetic orientation. They then plugged the energy measurements into a model equation with unknown parameters. This equation is used to describe the quantum interactions of an electron-nuclear spin defect under a magnetic field. Then, they could solve the equation to successfully characterize each defect.

    Locating and controlling

    After characterizing the defects, the next step was to characterize the interaction between the defects and the NV, which would simultaneously pinpoint their locations. To do so, they again swept the magnetic field at different orientations, but this time looked for changes in energies describing the interactions between the two defects and the NV center. The stronger the interaction, the closer they were to one another. They then used those interaction strengths to determine where the defects were located, in relation to the NV center and to each other. That generated a good map of the locations of all three defects in the diamond.

    Characterizing the defects and their interaction with the NV center allow for full control, which involves a few more steps to demonstrate. First, they pump the NV center and surrounding environment with a sequence of pulses of green light and microwaves that help put the three qubits in a well-known quantum state. Then, they use another sequence of pulses that ideally entangles the three qubits briefly, and then disentangles them, which enables them to detect the three-spin coherence of the qubits.

    The researchers verified the three-spin coherence by measuring a major spike in the resonance spectrum. The measurement of the spike recorded was essentially the sum of the frequencies of the three qubits. If the three qubits for instance had little or no entanglement, there would have been four separate spikes of smaller height.

    “We come into a black box [environment with each NV center]. But when we probe the NV environment, we start seeing dips and wonder which types of spins give us those dips. Once we [figure out] the spin of the unknown defects, and their interactions with the NV center, we can start controlling their coherence,” Sun says. “Then, we have full universal control of our quantum system.”

    Next, the researchers hope to better understand other environmental noise surrounding qubits. That will help them develop more robust error-correcting codes for quantum circuits. Furthermore, because on average the process of NV center creation in diamond creates numerous other spin defects, the researchers say they could potentially scale up the system to control even more qubits. “It gets more complex with scale. But if we can start finding NV centers with more resonance spikes, you can imagine starting to control larger and larger quantum systems,” Sun says.

    See the full article here .


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  • richardmitnick 10:33 am on March 8, 2020 Permalink | Reply
    Tags: "Doing machine learning the right way", Alexander Madry, Building machine-learning models that are more reliable understandable and robust., MIT, We approach an age where artificial intelligence will have great impact on many sectors of society.   

    From MIT News: “Doing machine learning the right way” 

    MIT News

    From MIT News

    March 7, 2020
    Rob Matheson

    1
    Alexander Madry.Image: Ian MacLellan.

    Professor Aleksander Madry strives to build machine-learning models that are more reliable, understandable, and robust.

    The work of MIT computer scientist Aleksander Madry is fueled by one core mission: “doing machine learning the right way.”

    Madry’s research centers largely on making machine learning — a type of artificial intelligence — more accurate, efficient, and robust against errors. In his classroom and beyond, he also worries about questions of ethical computing, as we approach an age where artificial intelligence will have great impact on many sectors of society.

    “I want society to truly embrace machine learning,” says Madry, a recently tenured professor in the Department of Electrical Engineering and Computer Science. “To do that, we need to figure out how to train models that people can use safely, reliably, and in a way that they understand.”

    Interestingly, his work with machine learning dates back only a couple of years, to shortly after he joined MIT in 2015. In that time, his research group has published several critical papers demonstrating that certain models can be easily tricked to produce inaccurate results — and showing how to make them more robust.

    In the end, he aims to make each model’s decisions more interpretable by humans, so researchers can peer inside to see where things went awry. At the same time, he wants to enable nonexperts to deploy the improved models in the real world for, say, helping diagnose disease or control driverless cars.

    “It’s not just about trying to crack open the machine-learning black box. I want to open it up, see how it works, and pack it back up, so people can use it without needing to understand what’s going on inside,” he says.

    For the love of algorithms

    Madry was born in Wroclaw, Poland, where he attended the University of Wroclaw as an undergraduate in the mid-2000s. While he harbored interest in computer science and physics, “I actually never thought I’d become a scientist,” he says.

    An avid video gamer, Madry initially enrolled in the computer science program with intentions of programming his own games. But in joining friends in a few classes in theoretical computer science and, in particular, theory of algorithms, he fell in love with the material. Algorithm theory aims to find efficient optimization procedures for solving computational problems, which requires tackling difficult mathematical questions. “I realized I enjoy thinking deeply about something and trying to figure it out,” says Madry, who wound up double-majoring in physics and computer science.

    When it came to delving deeper into algorithms in graduate school, he went to his first choice: MIT. Here, he worked under both Michel X. Goemans, who was a major figure in applied math and algorithm optimization, and Jonathan A. Kelner, who had just arrived to MIT as a junior faculty working in that field. For his PhD dissertation, Madry developed algorithms that solved a number of longstanding problems in graph algorithms, earning the 2011 George M. Sprowls Doctoral Dissertation Award for the best MIT doctoral thesis in computer science.

    After his PhD, Madry spent a year as a postdoc at Microsoft Research New England, before teaching for three years at the Swiss Federal Institute of Technology Lausanne — which Madry calls “the Swiss version of MIT.” But his alma mater kept calling him back: “MIT has the thrilling energy I was missing. It’s in my DNA.”

    Getting adversarial

    Shortly after joining MIT, Madry found himself swept up in a novel science: machine learning. In particular, he focused on understanding the re-emerging paradigm of deep learning. That’s an artificial-intelligence application that uses multiple computing layers to extract high-level features from raw input — such as using pixel-level data to classify images. MIT’s campus was, at the time, buzzing with new innovations in the domain.

    But that begged the question: Was machine learning all hype or solid science? “It seemed to work, but no one actually understood how and why,” Madry says.

    Answering that question set his group on a long journey, running experiment after experiment on deep-learning models to understand the underlying principles. A major milestone in this journey was an influential paper they published in 2018, developing a methodology for making machine-learning models more resistant to “adversarial examples.” Adversarial examples are slight perturbations to input data that are imperceptible to humans — such as changing the color of one pixel in an image — but cause a model to make inaccurate predictions. They illuminate a major shortcoming of existing machine-learning tools.

    Continuing this line of work, Madry’s group showed that the existence of these mysterious adversarial examples may contribute to how machine-learning models make decisions. In particular, models designed to differentiate images of, say, cats and dogs, make decisions based on features that do not align with how humans make classifications. Simply changing these features can make the model consistently misclassify cats as dogs, without changing anything in the image that’s really meaningful to humans.

    Results indicated some models — which may be used to, say, identify abnormalities in medical images or help autonomous cars identify objects in the road — aren’t exactly up to snuff. “People often think these models are superhuman, but they didn’t actually solve the classification problem we intend them to solve,” Madry says. “And their complete vulnerability to adversarial examples was a manifestation of that fact. That was an eye-opening finding.”

    That’s why Madry seeks to make machine-learning models more interpretable to humans. New models he’s developed show how much certain pixels in images the system is trained on can influence the system’s predictions. Researchers can then tweak the models to focus on pixels clusters more closely correlated with identifiable features — such as detecting an animal’s snout, ears, and tail. In the end, that will help make the models more humanlike — or “superhumanlike” — in their decisions. To further this work, Madry and his colleagues recently founded the MIT Center for Deployable Machine Learning, a collaborative research effort working toward building machine-learning tools ready for real-world deployment.

    “We want machine learning not just as a toy, but as something you can use in, say, an autonomous car, or health care. Right now, we don’t understand enough to have sufficient confidence in it for those critical applications,” Madry says.

    “We want machine learning not just as a toy, but as something you can use in, say, an autonomous car, or health care. Right now, we don’t understand enough to have sufficient confidence in it for those critical applications,” Madry says.

    Shaping education and policy

    Madry views artificial intelligence and decision making (“AI+D” is one of the three new academic units in the Department of Electrical Engineering and Computer Science) as “the interface of computing that’s going to have the biggest impact on society.”

    In that regard, he makes sure to expose his students to the human aspect of computing. In part, that means considering consequences of what they’re building. Often, he says, students will be overly ambitious in creating new technologies, but they haven’t thought through potential ramifications on individuals and society. “Building something cool isn’t a good enough reason to build something,” Madry says. “It’s about thinking about not if we can build something, but if we should build something.”

    Madry has also been engaging in conversations about laws and policies to help regulate machine learning. A point of these discussions, he says, is to better understand the costs and benefits of unleashing machine-learning technologies on society.

    “Sometimes we overestimate the power of machine learning, thinking it will be our salvation. Sometimes we underestimate the cost it may have on society,” Madry says. “To do machine learning right, there’s still a lot still left to figure out.”

    See the full article here .


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  • richardmitnick 9:25 am on March 6, 2020 Permalink | Reply
    Tags: , MIT,   

    From MIT News: “Showing robots how to do your chores” 

    MIT News

    From MIT News

    March 5, 2020
    Rob Matheson

    1
    Roboticists are developing automated robots that can learn new tasks solely by observing humans. At home, you might someday show a domestic robot how to do routine chores. Image: Christine Daniloff, MIT.

    By observing humans, robots learn to perform complex tasks, such as setting a table.

    Training interactive robots may one day be an easy job for everyone, even those without programming expertise. Roboticists are developing automated robots that can learn new tasks solely by observing humans. At home, you might someday show a domestic robot how to do routine chores. In the workplace, you could train robots like new employees, showing them how to perform many duties.

    Making progress on that vision, MIT researchers have designed a system that lets these types of robots learn complicated tasks that would otherwise stymie them with too many confusing rules. One such task is setting a dinner table under certain conditions.

    At its core, the researchers’ “Planning with Uncertain Specifications” (PUnS) system gives robots the humanlike planning ability to simultaneously weigh many ambiguous — and potentially contradictory — requirements to reach an end goal. In doing so, the system always chooses the most likely action to take, based on a “belief” about some probable specifications for the task it is supposed to perform.

    In their work, the researchers compiled a dataset with information about how eight objects — a mug, glass, spoon, fork, knife, dinner plate, small plate, and bowl — could be placed on a table in various configurations. A robotic arm first observed randomly selected human demonstrations of setting the table with the objects. Then, the researchers tasked the arm with automatically setting a table in a specific configuration, in real-world experiments and in simulation, based on what it had seen.

    To succeed, the robot had to weigh many possible placement orderings, even when items were purposely removed, stacked, or hidden. Normally, all of that would confuse robots too much. But the researchers’ robot made no mistakes over several real-world experiments, and only a handful of mistakes over tens of thousands of simulated test runs.

    “The vision is to put programming in the hands of domain experts, who can program robots through intuitive ways, rather than describing orders to an engineer to add to their code,” says first author Ankit Shah, a graduate student in the Department of Aeronautics and Astronautics (AeroAstro) and the Interactive Robotics Group, who emphasizes that their work is just one step in fulfilling that vision. “That way, robots won’t have to perform preprogrammed tasks anymore. Factory workers can teach a robot to do multiple complex assembly tasks. Domestic robots can learn how to stack cabinets, load the dishwasher, or set the table from people at home.”

    Joining Shah on the paper are AeroAstro and Interactive Robotics Group graduate student Shen Li and Interactive Robotics Group leader Julie Shah, an associate professor in AeroAstro and the Computer Science and Artificial Intelligence Laboratory.

    Bots hedging bets

    Robots are fine planners in tasks with clear “specifications,” which help describe the task the robot needs to fulfill, considering its actions, environment, and end goal. Learning to set a table by observing demonstrations, is full of uncertain specifications. Items must be placed in certain spots, depending on the menu and where guests are seated, and in certain orders, depending on an item’s immediate availability or social conventions. Present approaches to planning are not capable of dealing with such uncertain specifications.

    A popular approach to planning is “reinforcement learning,” a trial-and-error machine-learning technique that rewards and penalizes them for actions as they work to complete a task. But for tasks with uncertain specifications, it’s difficult to define clear rewards and penalties. In short, robots never fully learn right from wrong.

    The researchers’ system, called PUnS (for Planning with Uncertain Specifications), enables a robot to hold a “belief” over a range of possible specifications. The belief itself can then be used to dish out rewards and penalties. “The robot is essentially hedging its bets in terms of what’s intended in a task, and takes actions that satisfy its belief, instead of us giving it a clear specification,” Ankit Shah says.

    The system is built on “linear temporal logic” (LTL), an expressive language that enables robotic reasoning about current and future outcomes. The researchers defined templates in LTL that model various time-based conditions, such as what must happen now, must eventually happen, and must happen until something else occurs. The robot’s observations of 30 human demonstrations for setting the table yielded a probability distribution over 25 different LTL formulas. Each formula encoded a slightly different preference — or specification — for setting the table. That probability distribution becomes its belief.

    “Each formula encodes something different, but when the robot considers various combinations of all the templates, and tries to satisfy everything together, it ends up doing the right thing eventually,” Ankit Shah says.

    Following criteria

    The researchers also developed several criteria that guide the robot toward satisfying the entire belief over those candidate formulas. One, for instance, satisfies the most likely formula, which discards everything else apart from the template with the highest probability. Others satisfy the largest number of unique formulas, without considering their overall probability, or they satisfy several formulas that represent highest total probability. Another simply minimizes error, so the system ignores formulas with high probability of failure.

    Designers can choose any one of the four criteria to preset before training and testing. Each has its own tradeoff between flexibility and risk aversion. The choice of criteria depends entirely on the task. In safety critical situations, for instance, a designer may choose to limit possibility of failure. But where consequences of failure are not as severe, designers can choose to give robots greater flexibility to try different approaches.

    With the criteria in place, the researchers developed an algorithm to convert the robot’s belief — the probability distribution pointing to the desired formula — into an equivalent reinforcement learning problem. This model will ping the robot with a reward or penalty for an action it takes, based on the specification it’s decided to follow.

    In simulations asking the robot to set the table in different configurations, it only made six mistakes out of 20,000 tries. In real-world demonstrations, it showed behavior similar to how a human would perform the task. If an item wasn’t initially visible, for instance, the robot would finish setting the rest of the table without the item. Then, when the fork was revealed, it would set the fork in the proper place. “That’s where flexibility is very important,” Ankit Shah says. “Otherwise it would get stuck when it expects to place a fork and not finish the rest of table setup.”

    Next, the researchers hope to modify the system to help robots change their behavior based on verbal instructions, corrections, or a user’s assessment of the robot’s performance. “Say a person demonstrates to a robot how to set a table at only one spot. The person may say, ‘do the same thing for all other spots,’ or, ‘place the knife before the fork here instead,’” Ankit Shah says. “We want to develop methods for the system to naturally adapt to handle those verbal commands, without needing additional demonstrations.”

    See the full article here .


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